feat: 支持腾讯混元向量模型

apps/setting/models_provider/impl/tencent_model_provider/credential/embedding.py

@@ -1,11 +1,5 @@
-import json
 from typing import Dict
 
-from tencentcloud.common import credential
-from tencentcloud.common.profile.client_profile import ClientProfile
-from tencentcloud.common.profile.http_profile import HttpProfile
-from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
-
 from common import forms
 from common.exception.app_exception import AppApiException
 from common.forms import BaseForm
@@ -13,48 +7,24 @@ from setting.models_provider.base_model_provider import BaseModelCredential, Val
 
 
 class TencentEmbeddingCredential(BaseForm, BaseModelCredential):
-    @classmethod
-    def _validate_model_type(cls, model_type: str, provider) -> bool:
-        model_type_list = provider.get_model_type_list()
-        if not any(mt.get('value') == model_type for mt in model_type_list):
-            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
-        return True
-
-    @classmethod
-    def _validate_credential(cls, model_credential: Dict[str, object]) -> credential.Credential:
-        for key in ['SecretId', 'SecretKey']:
-            if key not in model_credential:
-                raise AppApiException(ValidCode.valid_error.value, f'{key} 字段为必填字段')
-        return credential.Credential(model_credential['SecretId'], model_credential['SecretKey'])
-
-    @classmethod
-    def _test_credentials(cls, client, model_name: str):
-        req = models.GetEmbeddingRequest()
-        params = {
-            "Model": model_name,
-            "Input": "测试"
-        }
-        req.from_json_string(json.dumps(params))
-        try:
-            res = client.GetEmbedding(req)
-            print(res.to_json_string())
-        except Exception as e:
-            raise AppApiException(ValidCode.valid_error.value, f'校验失败,请检查参数是否正确: {str(e)}')
 
     def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
                  raise_exception=True) -> bool:
+        model_type_list = provider.get_model_type_list()
+        if not any(list(filter(lambda mt: mt.get('value') == model_type, model_type_list))):
+            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+        self.valid_form(model_credential)
         try:
-            self._validate_model_type(model_type, provider)
+            model = provider.get_model(model_type, model_name, model_credential)
-            cred = self._validate_credential(model_credential)
+            model.embed_query('你好')
-            httpProfile = HttpProfile(endpoint="hunyuan.tencentcloudapi.com")
+        except Exception as e:
-            clientProfile = ClientProfile(httpProfile=httpProfile)
+            if isinstance(e, AppApiException):
-            client = hunyuan_client.HunyuanClient(cred, "", clientProfile)
-            self._test_credentials(client, model_name)
-            return True
-        except AppApiException as e:
-            if raise_exception:
                 raise e
-            return False
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'校验失败,请检查参数是否正确: {str(e)}')
+            else:
+                return False
+        return True
 
     def encryption_dict(self, model: Dict[str, object]) -> Dict[str, object]:
         encrypted_secret_key = super().encryption(model.get('SecretKey', ''))

apps/setting/models_provider/impl/tencent_model_provider/model/embedding.py

@@ -1,25 +1,34 @@
-from setting.models_provider.base_model_provider import MaxKBBaseModel
+from typing import Dict, List
-from typing import Dict
+
-import requests
+from langchain_core.embeddings import Embeddings
+from tencentcloud.common import credential
+from tencentcloud.hunyuan.v20230901.hunyuan_client import HunyuanClient
+from tencentcloud.hunyuan.v20230901.models import GetEmbeddingRequest
 
 
-class TencentEmbeddingModel(MaxKBBaseModel):
+class TencentEmbeddingModel(Embeddings):
-    def __init__(self, secret_id: str, secret_key: str, api_base: str, model_name: str):
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return [self.embed_query(text) for text in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        request = GetEmbeddingRequest()
+        request.Input = text
+        res = self.client.GetEmbedding(request)
+        return res.Data
+
+    def __init__(self, secret_id: str, secret_key: str, model_name: str):
         self.secret_id = secret_id
         self.secret_key = secret_key
-        self.api_base = api_base
         self.model_name = model_name
+        cred = credential.Credential(
+            secret_id, secret_key
+        )
+        self.client = HunyuanClient(cred, "")
 
     @staticmethod
     def new_instance(model_type: str, model_name: str, model_credential: Dict[str, str], **model_kwargs):
         return TencentEmbeddingModel(
             secret_id=model_credential.get('SecretId'),
             secret_key=model_credential.get('SecretKey'),
-            api_base=model_credential.get('api_base'),
             model_name=model_name,
         )
-
-
-    def _generate_auth_token(self):
-        # Example method to generate an authentication token for the model API
-        return f"{self.secret_id}:{self.secret_key}"

apps/setting/models_provider/impl/tencent_model_provider/tencent_model_provider.py

@@ -70,7 +70,7 @@ def _initialize_model_info():
 
     tencent_embedding_model_info = _create_model_info(
         'hunyuan-embedding',
-        '',
+        '腾讯混元 Embedding 接口，可以将文本转化为高质量的向量数据。向量维度为1024维。',
         ModelTypeConst.EMBEDDING,
         TencentEmbeddingCredential,
         TencentEmbeddingModel
@@ -80,6 +80,7 @@ def _initialize_model_info():
 
     model_info_manage = ModelInfoManage.builder() \
         .append_model_info_list(model_info_list) \
+        .append_model_info_list(model_info_embedding_list) \
         .append_default_model_info(model_info_list[0]) \
         .build()